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pro vyhledávání: '"Lawless, Amos S."'
In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a least-squares minimization problem . In recent years, we have seen the popularity of hybrid v
Externí odkaz:
http://arxiv.org/abs/2306.11869
Data assimilation combines prior (or background) information with observations to estimate the initial state of a dynamical system over a given time-window. A common application is in numerical weather prediction where a previous forecast and atmosph
Externí odkaz:
http://arxiv.org/abs/2107.12361
Publikováno v:
On time-parallel preconditioning for the state formulation of incremental weak constraint 4D-Var. 2021
Using a high degree of parallelism is essential to perform data assimilation efficiently. The state formulation of the incremental weak constraint four-dimensional variational data assimilation method allows parallel calculations in the time dimensio
Externí odkaz:
http://arxiv.org/abs/2105.09802
There is growing awareness that errors in the model equations cannot be ignored in data assimilation methods such as four-dimensional variational assimilation (4D-Var). If allowed for, more information can be extracted from observations, longer time
Externí odkaz:
http://arxiv.org/abs/2101.07249
Data assimilation algorithms combine prior and observational information, weighted by their respective uncertainties, to obtain the most likely posterior of a dynamical system. In variational data assimilation the posterior is computed by solving a n
Externí odkaz:
http://arxiv.org/abs/2010.08416
Akademický článek
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We consider the large-sparse symmetric linear systems of equations that arise in the solution of weak constraint four-dimensional variational data assimilation, a method of high interest for numerical weather prediction. These systems can be written
Externí odkaz:
http://arxiv.org/abs/1908.07949
Autor:
Tabeart, Jemima M., Dance, Sarah L., Lawless, Amos S., Migliorini, Stefano, Nichols, Nancy K., Smith, Fiona, Waller, Joanne A.
Recent developments in numerical weather prediction have led to the use of correlated observation error covariance (OEC) information in data assimilation and forecasting systems. However, diagnosed OEC matrices are often ill-conditioned and may cause
Externí odkaz:
http://arxiv.org/abs/1908.04071
Akademický článek
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High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures. As observation error covariance matrices are often obtained by sampling metho
Externí odkaz:
http://arxiv.org/abs/1810.10984